AI transformation · step 3
Training your AI on your own calls
Off-the-shelf AI answers like the market average: it doesn't know your product, your tone, your typical objections or your terms. CallScribe closes that gap — on top of a knowledge base and tagged calls you configure and calibrate the bot, the secretary, and your extraction and auto-tagging rules, while real historical dialogues serve as your reference and test set. Analytics shows where the AI is wrong, and you refine it on your own specifics — not on the "market average."
The problem
An out-of-the-box voice bot or secretary sounds like a stranger: it doesn't know your product, can't tell your plans and terms apart, answers objections with generic lines and confuses its boundaries — when to answer itself and when to hand off to an operator. Customers feel it, and the first non-standard question breaks the dialogue.
You can't configure it blind either. The script is written from memory, objection responses are invented "as they seem right," and there's nowhere to test any of it — except by pushing the bot onto live calls and hoping. Mistakes surface on real customers, and fixes go slowly and by guesswork.
The cause: all your specifics — the real phrasings, common objections, the operators' winning answers, the brand tone — already live in your calls, yet none of it transfers into the AI. Without that reference, any bot configuration stays a guess and quality has nothing to be measured against.
How the platform solves it
- 1
Build a knowledge base from calls
The AI canvas fills your reference docs with real data from your recordings — common topics, typical objections, operators' winning phrasings, product and plans. This is exactly the context off-the-shelf AI lacks.
- 2
Configure the bot and secretary scenarios
On top of the knowledge base you calibrate the agent's persona, goal and greeting, its objection responses, the fields it collects and its boundaries — when to answer itself and when to hand off. All tuned to your specifics, not to an averaged template.
- 3
Test against historical dialogues
Real past calls become the test set: you run the scenarios and extraction rules over the archive as a reference and see how the AI would behave on genuine conversations — before it ever reaches the live line.
- 4
Find the mistakes in analytics
Dashboards and quality metrics show where the AI misses: an unrecognized topic, a weak objection response, a wrongly extracted field or a mistagged call. The errors are visible concretely, not "by ear."
- 5
Refine and close the loop
You adjust scenarios, responses, extraction and tagging rules — and run them over the calls again. Every new conversation feeds the base and the test set: "call → analysis → improvement → new call" keeps turning on its own.
The result
The output is AI trained on your specifics, not on the "market average": the bot and secretary speak in your tone, know your product and objections, and you measure and lift quality on a closed loop.
- The bot and secretary are tuned to your product, tone and typical objections, not to an averaged template.
- Historical calls act as a reference and test set: quality is checked before the live line.
- The AI's mistakes are visible concretely in analytics — you know what and where to refine.
- A closed loop: every new conversation improves the bot, extraction and tagging rules on its own.
Key facts
- What gets calibrated
- Scenarios, persona, goal, objection responses, collected fields and boundaries of the bot and secretary, plus extraction and auto-tagging rules.
- Test set
- Your historical calls are the reference: scenarios and rules are checked against real past dialogues before going to the live line.
- Where to find mistakes
- In dashboards and quality metrics: where the AI missed a topic, answered an objection weakly, extracted a field wrong or applied the wrong tag.
- Source of knowledge
- A knowledge base built from calls (AI canvas) — real topics, objections and phrasings that off-the-shelf AI lacks.
- What it is NOT
- This is not fine-tuning model weights, but calibrating scenarios, knowledge and rules to your specifics.
FAQ
Frequently asked questions
Is this fine-tuning of the model itself?
No. We don't retrain model weights. "Training" here means calibrating the bot and secretary scenarios, filling a knowledge base with your data, and configuring extraction and auto-tagging rules to your specifics. That context is what makes the answers "yours" rather than averaged.
How do I test the bot without pushing it onto live calls?
Your historical dialogues work as a reference and test set. You run the configured scenarios and extraction rules over the archive and see how the AI would behave on real conversations — and quality metrics show where it's wrong, before it reaches the live line.
How does the bot learn our product and objections?
From the knowledge base built from calls. The AI canvas fills your reference docs with real topics, common objections and operators' winning phrasings from your recordings, and those documents are then connected to scenarios and AI actions as context.
What does the closed loop give me?
Every new agent call re-enters the analysis pipeline — transcription, metrics, tags — feeds the knowledge base and test set, and surfaces new mistakes. So "call → analysis → improvement → new call" keeps turning continuously, and the AI grows more accurate without betting blind.
Ready to start?
Turn every conversation into data, knowledge and action
Start by analyzing your conversations — no risk, no bots required. The platform turns your archive into data and a knowledge base, and voice agents plug in when you're ready.
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